Publications / 2017 Proceedings of the 34rd ISARC, Taipei, Taiwan

Efficient Face Recognition Using FPGA and Semantic Features for Security Controls

Ying-Hao Yu, Yu-Chen Xu, Yi-Siang Ting and Ngaiming Kwok
Pages 1022-1029 (2017 Proceedings of the 34rd ISARC, Taipei, Taiwan, ISBN 978-80-263-1371-7, ISSN 2413-5844)

In a security control system, facial features are indispensable components to biometrics. An effective face recognition system is usually composed of facial features’ transformation, geometric analysis, and recursive training for deep learning networks in order to counteract the deceit from spurious targets. Nowadays design difficulty of face recognition arises from the dilemma of lower computing resource usage, power consumption, and delay time to install system ubiquitously in a building. In this paper, a chip-based face recognition is proposed by using semantic features to resolve aforementioned problem. The proposed system does not adopt tedious training processes but employs semantic features to achieve a miniature database and absolutely real-time processing speed. Our experimental results indicate that the proposed face recognition system can efficiently discriminate target from peoples’ faces only with economical resource usage on chip.

Keywords: Face recognition, Semantic features, FPGA, Security control, Biometrics